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Summary of Scalable Interactive Machine Learning For Future Command and Control, by Anna Madison et al.


Scalable Interactive Machine Learning for Future Command and Control

by Anna Madison, Ellen Novoseller, Vinicius G. Goecks, Benjamin T. Files, Nicholas Waytowich, Alfred Yu, Vernon J. Lawhern, Steven Thurman, Christopher Kelshaw, Kaleb McDowell

First submitted to arxiv on: 9 Feb 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper proposes the integration of artificial and human intelligence to revolutionize the Command and Control (C2) operations process. It leverages interactive machine learning, where humans guide machine learning algorithm behavior, to address the need for robust decision-making processes in rapidly changing operational environments. The paper identifies gaps in current science and technology that future work should address to enable scalable interactive machine learning (SIML). This includes developing human-AI interaction algorithms for planning in complex situations, fostering resilient human-AI teams through role optimization, and scaling algorithms and teams across various contexts.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about using artificial intelligence to help humans make better decisions in war. Right now, commanders have to make quick decisions in confusing situations, which can be hard. The authors think that by working together with machines, we can create a better system for making decisions. They highlight three areas where more research is needed: how people and AI work together, creating strong teams between humans and AI, and scaling up this cooperation across different situations.

Keywords

* Artificial intelligence  * Machine learning  * Optimization